Symbolic Expression

Symbolic expression, the representation of information using discrete symbols, is a core area of artificial intelligence research aiming to create more interpretable and robust systems. Current research focuses on integrating symbolic methods with connectionist approaches like neural networks and large language models, particularly to improve reasoning, planning, and explainability in areas such as reinforcement learning and natural language processing. This work is significant because it addresses limitations of purely data-driven models, leading to more reliable, understandable, and potentially safer AI systems across various applications, including robotics, cybersecurity, and scientific discovery.